Python

πN³S (pronounced pi-N-S) is the CATS framework for training and using Physics-Informed Neural Networks (PINNs).

Semantic description of computational material science samples including crystallographic defects (on the atomic scale).

https://how-to-create-publishable-netcdf-data-6d8371.pages.rwth-aachen.de/

Measurement software of the EG-Lab

Additional content for LECO communication using PyLECO.

This project provides a simple static site generator for resources in Coscine. The resulting site can be hosted on GitHub/GitLab pages (for free) or on a dedicated webserver. Coscine project members can create such a site to make their data accessible to external users or to build custom interfaces around it.

A Python Software Development Kit for Coscine

THG-Simulation

This is the Python plotID project. Documentation

Measurement scripts and instrument actors for the THG project

Create plots in multiple uniform designs for scientific publications.

Support software for the 6-channel Diskriminator to be used in the VDC experiment.

Control the air conditioning, e.g. for the lab.

Examples and exercises for the python part of the lecture "Introduction to Python and Data Science for Materials Science"

Tools, helpers and more common to RWTH Jupyter notebooks: https://jupyter.pages.rwth-aachen.de/rwth-nb/